Create app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("Simbolo-Servicio/Myanmarsar-GPT")
+model = AutoModelForCausalLM.from_pretrained("Simbolo-Servicio/Myanmarsar-GPT")
+
+# Use GPU if available
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+# Define generation function
+def generate_text(prompt, max_length=100, temperature=0.7):
+    input_ids = tokenizer.encode(prompt, return_tensors='pt').to(device)
+    output = model.generate(
+        input_ids,
+        max_length=max_length,
+        do_sample=True,
+        temperature=temperature,
+        top_k=50,
+        top_p=0.95
+    )
+    result = tokenizer.decode(output[0], skip_special_tokens=True)
+    return result
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=generate_text,
+    inputs=[
+        gr.Textbox(lines=2, label="Myanmar Prompt"),
+        gr.Slider(20, 300, step=10, label="Max Length", value=100),
+        gr.Slider(0.1, 1.5, step=0.1, label="Temperature", value=0.7)
+    ],
+    outputs="text",
+    title="Myanmarsar-GPT Text Generator",
+    description="Type Burmese text and let the model generate a continuation."
+)
+
+iface.launch()